As we have stated in Section , agent embeddings are pooled together to make the final graph-level decision. In practice, following [1]} we pool agent embeddings after each agent update step and sum the individual step predictions to make the final prediction:
\(o_{a_i}^t = \phi _{o}\left(a_i^t\right) ,\) 
\(\overline{o} = \sum _{t} f_{o}\left( \frac{1}{k} \sum _{a_i \in A} o_{a_i}^t, \, \max {\lbrace o_{a_i}^t\ \quad \text{for} \quad a_i \in A \rbrace } \right).\) 
